from utils import print_time_report, print_results, plot_results
print_time_report()
| hour | min | sec | |
|---|---|---|---|
| algo | |||
| KNeighborsClassifier | 0 | 13 | 15 |
| daal4py_KNeighborsClassifier | 0 | 5 | 15 |
| KNeighborsClassifier_kd_tree | 0 | 5 | 37 |
| daal4py_KNeighborsClassifier_kd_tree | 0 | 1 | 34 |
| KMeans | 0 | 21 | 29 |
| daal4py_KMeans | 0 | 8 | 26 |
| total | 0 | 55 | 39 |
print_results(algo="KNeighborsClassifier", versus_lib="daal4py")
plot_results(algo="KNeighborsClassifier", versus_lib="daal4py", group_by_cols=["algorithm", "n_neighbors", "function"], split_hist_by=["n_jobs"])
print_results(algo="KNeighborsClassifier_kd_tree", versus_lib="daal4py")
plot_results(algo="KNeighborsClassifier_kd_tree", versus_lib="daal4py", group_by_cols=["algorithm", "n_neighbors", "function"], split_hist_by=["n_jobs"])
from utils import _make_dataset
data = _make_dataset('KMeans', 'daal4py', compare_cols=["n_iter"])
data[data["function"] == "fit"].sort_values(["n_iter_sklearn", "n_iter_daal4py"])
| estimator | lib | function | n_samples | n_features | init | max_iter | n_clusters | n_init | tol | adjusted_rand_score | mean_sklearn | stdev_sklearn | n_iter_sklearn | mean_daal4py | stdev_daal4py | n_iter_daal4py | speedup | stdev_speedup | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 18 | KMeans | sklearn | fit | 10000 | 100 | k-means++ | 30 | 3 | 1 | 0.0 | NaN | 0.5007 | 0.0048 | 25.0 | 0.1339 | 0.0012 | 22.0 | 3.74 | 4.00 |
| 0 | KMeans | sklearn | fit | 10000 | 2 | k-means++ | 30 | 3 | 1 | 0.0 | NaN | 0.0917 | 0.0237 | 30.0 | 0.0041 | 0.0004 | 28.0 | 22.37 | 59.25 |
| 9 | KMeans | sklearn | fit | 10000 | 2 | random | 30 | 3 | 1 | 0.0 | NaN | 0.0865 | 0.0228 | 30.0 | 0.0083 | 0.0007 | 30.0 | 10.42 | 32.57 |
| 27 | KMeans | sklearn | fit | 10000 | 100 | random | 30 | 3 | 1 | 0.0 | NaN | 0.0161 | 0.0006 | 30.0 | 0.0037 | 0.0002 | 30.0 | 4.35 | 3.00 |
| 36 | KMeans | sklearn | fit | 1000000 | 2 | k-means++ | 30 | 3 | 1 | 0.0 | NaN | 0.0245 | 0.0008 | 30.0 | 0.0061 | 0.0005 | 30.0 | 4.02 | 1.60 |
| 45 | KMeans | sklearn | fit | 1000000 | 2 | random | 30 | 3 | 1 | 0.0 | NaN | 0.3591 | 0.0644 | 30.0 | 0.0727 | 0.0031 | 30.0 | 4.94 | 20.77 |
| 54 | KMeans | sklearn | fit | 1000000 | 100 | k-means++ | 30 | 3 | 1 | 0.0 | NaN | 0.1503 | 0.0058 | 30.0 | 0.0248 | 0.0010 | 30.0 | 6.06 | 5.80 |
| 63 | KMeans | sklearn | fit | 1000000 | 100 | random | 30 | 3 | 1 | 0.0 | NaN | 0.2000 | 0.0055 | 30.0 | 0.0359 | 0.0008 | 30.0 | 5.57 | 6.87 |
| 3 | KMeans | sklearn | fit | 10000 | 2 | k-means++ | 30 | 10 | 1 | 0.0 | NaN | 0.0004 | 0.0001 | NaN | 0.0004 | 0.0003 | NaN | 1.00 | 0.33 |
| 6 | KMeans | sklearn | fit | 10000 | 2 | k-means++ | 30 | 300 | 1 | 0.0 | NaN | 0.0004 | 0.0001 | NaN | 0.0002 | 0.0001 | NaN | 2.00 | 1.00 |
| 12 | KMeans | sklearn | fit | 10000 | 2 | random | 30 | 10 | 1 | 0.0 | NaN | 0.0004 | 0.0001 | NaN | 0.0002 | 0.0001 | NaN | 2.00 | 1.00 |
| 15 | KMeans | sklearn | fit | 10000 | 2 | random | 30 | 300 | 1 | 0.0 | NaN | 0.0003 | 0.0001 | NaN | 0.0002 | 0.0001 | NaN | 1.50 | 1.00 |
| 21 | KMeans | sklearn | fit | 10000 | 100 | k-means++ | 30 | 10 | 1 | 0.0 | NaN | 0.0010 | 0.0002 | NaN | 0.0007 | 0.0002 | NaN | 1.43 | 1.00 |
| 24 | KMeans | sklearn | fit | 10000 | 100 | k-means++ | 30 | 300 | 1 | 0.0 | NaN | 0.0003 | 0.0001 | NaN | 0.0002 | 0.0001 | NaN | 1.50 | 1.00 |
| 30 | KMeans | sklearn | fit | 10000 | 100 | random | 30 | 10 | 1 | 0.0 | NaN | 0.0004 | 0.0001 | NaN | 0.0002 | 0.0001 | NaN | 2.00 | 1.00 |
| 33 | KMeans | sklearn | fit | 10000 | 100 | random | 30 | 300 | 1 | 0.0 | NaN | 0.0004 | 0.0001 | NaN | 0.0002 | 0.0001 | NaN | 2.00 | 1.00 |
| 39 | KMeans | sklearn | fit | 1000000 | 2 | k-means++ | 30 | 10 | 1 | 0.0 | NaN | 0.0004 | 0.0001 | NaN | 0.0002 | 0.0001 | NaN | 2.00 | 1.00 |
| 42 | KMeans | sklearn | fit | 1000000 | 2 | k-means++ | 30 | 300 | 1 | 0.0 | NaN | 0.0005 | 0.0005 | NaN | 0.0002 | 0.0001 | NaN | 2.50 | 5.00 |
| 48 | KMeans | sklearn | fit | 1000000 | 2 | random | 30 | 10 | 1 | 0.0 | NaN | 0.0009 | 0.0002 | NaN | 0.0007 | 0.0001 | NaN | 1.29 | 2.00 |
| 51 | KMeans | sklearn | fit | 1000000 | 2 | random | 30 | 300 | 1 | 0.0 | NaN | 0.0004 | 0.0001 | NaN | 0.0002 | 0.0001 | NaN | 2.00 | 1.00 |
| 57 | KMeans | sklearn | fit | 1000000 | 100 | k-means++ | 30 | 10 | 1 | 0.0 | NaN | 0.0006 | 0.0001 | NaN | 0.0003 | 0.0001 | NaN | 2.00 | 1.00 |
| 60 | KMeans | sklearn | fit | 1000000 | 100 | k-means++ | 30 | 300 | 1 | 0.0 | NaN | 0.0004 | 0.0002 | NaN | 0.0002 | 0.0001 | NaN | 2.00 | 2.00 |
| 66 | KMeans | sklearn | fit | 1000000 | 100 | random | 30 | 10 | 1 | 0.0 | NaN | 0.0007 | 0.0001 | NaN | 0.0004 | 0.0001 | NaN | 1.75 | 1.00 |
| 69 | KMeans | sklearn | fit | 1000000 | 100 | random | 30 | 300 | 1 | 0.0 | NaN | 0.0004 | 0.0002 | NaN | 0.0002 | 0.0001 | NaN | 2.00 | 2.00 |
print_results(algo="KMeans", versus_lib="daal4py", compare_cols=["n_iter"])
plot_results(algo="KMeans", versus_lib="daal4py", group_by_cols=["init", "max_iter", "n_clusters", "n_init", "tol", "function"], compare_cols=["n_iter"])